The present embodiments relate to hemodynamic quantification in medical imaging. Blood-flow computations provide important insights into the structure and function of the cardiovascular system. For coronary artery disease (CAD), the functional index of fractional flow reserve (FFR) has been predicted from medical imaging by employing computational fluid dynamics (CFD). These CFD-based models combine geometrical information extracted from medical imaging with background knowledge on the physiology of the system, encoded in a complex mathematical fluid flow model consisting of partial differential equations. This approach leads to a large number of algebraic equations, making it computationally very demanding, preventing adoption of this technology for real-time applications such as intra-operative guidance of interventions. An alternative and less computationally expensive approach is based on machine learning (ML) algorithms. The relationship between input data and quantities of interest (e.g., FFR) is represented by a model built from a database of samples with known characteristics and outcome.
The accuracy of the predictions depends on the quality and accuracy or precision of the input information, as well as on the assumptions of the models. The main source of uncertainty for quantities of interest extracted from patient-specific blood flow computations may be represented by the anatomical model reconstructed from medical images. The resolution and precision of the acquisition scans, the segmentation, the reconstruction, and specific patient conditions (e.g., age, gender, or BMI) represent the some causes for the uncertainties. The predicted quantity has an unknown level of accuracy, making it more difficult for the physician to use the quantification.
Previous approaches directed at estimating the geometric sensitivity typically focused on the influence of the geometric uncertainty on the FFR values in the same region. This information may assist in deciding whether to use a quantification, but does not provide information that may be used to improve the quantification for that patient.